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Field
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, such as R, Python, and statistics, and to learn collaboration and leadership skills through workshop and group experience. USDA-ARS Mentor: If you have questions about the nature of the research, please
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responsibility is to build and validate these offloading strategies, complete with Python application programming interfaces (APIs), through rigorous simulations and proof-of-concept (PoC) trials. This position is
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optics, near-infrared spectroscopy (NIRS), diffuse optics, photonics or related areas. Experience in scientific software development and programming (e.g. Python, MATLAB, C/C++, LabVIEW). Experience in
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, computer science, physics, data science or a closely related discipline. Experience in MRI image analysis and scientific programming, preferably using Python and modern computational libraries. Experience applying
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optimized for single-molecule mechanical manipulation. Develop, refine, and implement automated data analysis pipelines using Igor Pro (or MATLAB, Python). Mentor graduate and undergraduate students within
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. Strong research, data-management, and analytical skills, with proficiency in software such as Stata, R, Python, or equivalent tools. Excellent written and oral communication skills, including the ability
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machine-learning, predictive-modelling or multivariate methods to behavioural and/or EEG data. Proficiency in R, MATLAB and Python. What we can offer In return we offer a generous pension, relocation
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of multiplex cytometry data analysis programs including OMIQ Experience in statistical programming with R and/or Python. Knowledge of R/Python spatial transcriptomics data analysis packages such as Seurat/Giotto
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• PhD in Mathematics/Statistics/CS. Skills: • Proficiency in reinforcement learning, quantitative finance, risk management, and machine learning. • Strong programming skill in Python
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computational methods for engineering analysis. Proficiency in scientific programming, preferably using Python, MATLAB, C/C++, or comparable programming languages. Experience with advanced sampling methods